Cross-Pollinate Your Design Ideas With AI : The Smart Ideation Framework

Introduction

Many times, UX designers have felt stuck for hours trying to research and navigate to a perfect approach that would be a custom fit into the context that we are working on, leaving us staring at a blank screen, or just pacing around trying to look in each corner of our brain.

Lets dive into the Smart Ideation Framework!

The Idea

Now, imagine having a creative partner by your side, one that suggests, critiques, and even borrows ideas from other industries at lightning speed. With AI stepping up from an assistant to a collaborator, this is the proposed Smart Ideation Framework in action. This entails

• Using LLMs and NLPs for systemic explorations
• Breaking through creative blocks by clearing the mental cache
• Leveraging the speed of AI for cross pollination of ideas across different industries and domains

The Smart Ideation Framework – A structure to build your flow!

The Smart Ideation Framework is a way of looking at what can be achieved, improved and enhanced by  transforming AI into a partner for strategy and innovation rather than just an assistant for basic everyday chores, such as drafting emails, paraphrasing UX copies, etc. We are now talking about leveraging AI rather than just using AI tools.

Let me further illustrate this with an example – Suppose you are  working on a mobile banking app. Instead of just asking an AI tool to generate user flows, you ask the AI, “How do other industries onboard anxious users?” With this slight change in the intent of the question, produces a response with onboarding tricks from healthcare or fitness.

This points to the evolution of usage of AI,  meant to be explored and leveraged in ways that align problem solving with diverse as well as filtered responses, all this while cutting down the research and reference finding time, substantially.

Does it benefit us in practice?

• • Overcoming Creative Blocks

    Example: While working on error messages for one of the clients, for an HRMS platform, I felt stuck in striking the balance between urgency of the notification, while keeping it soft and not making it feel forceful to the users. I asked Claude for softer approaches borrowed from a hospitality website. This instantly turned my warning like messages to a kinder and more empathetic push for the user.

  • Cross-Pollinate Ideas

    Anecdote: As a product designer I was once intrigued by the questione: “How do chefs guide diners through a tasting menu?” With the help of AI, this question inspired a CRM product’s new feature tour, making onboarding feel more like exploration than instruction.

  • Systematic Exploration

    Example: Rather than choosing the first onboarding flow, AI helped me rapidly generate and compare three paths: one focused on speed, one on visual storytelling, and one that made a long multi step setup a breeze to get through! This helped me broaden the client’s understanding, and the final design evolved to be a mix of the best elements from all three.

  • Critical Thinking Partner

    Anecdote: During a brainstorming session for a new product’s feature offerings (without any testing data available), the AI tool we were exploring “challenged” our team’s assumptions about what features users would care about most. This made us realise that we were discussing in loops, on the basis of internal biases, not real user pain points. Gaining this clarity prompted us for a new round of user interviews with targeted directions to gain specific insights.

Upon exploring further, I began considering: What does AI do better than us humans?

Through multiple ideation and discussion sessions with the fellow designers, trying to optimise workflows with best suited AI tools. We experimented and tested out multiple tools and agents that could be leveraged at different stages of the Design Process. While reviewing the responses from these tools, we found a common pattern that identified what AI did better than us humans and also where it fell short!
Drawing from this experimentation and also from available information online, a few things stood out – strengths and limitations of our AI buddy.

The Strengths

• Organizes complex problemsExample: While mapping a multi-step sign-up, AI almost instantly broke it down into logical stages and flagged spots where users might drop off, helping me focus attention on weak areas rather than over optimizing steps that already worked well!
• Spots patterns across domainsAnecdote: I was knee deep into all the problem areas and client exceptions, while designing a new report for the HRMS platform, AI highlighted patterns from trading platform dashboard flows that visualized large number of data points. This helped me to simplify the visualisation and interactions on the report, while providing value addition for both user as well as the client, who gained more insights on the user behaviour. This also became the ideation platform for both new feature addition as well as upgrading the existing user flows.

The Limitations

• Surface-level cultural understanding
  Example: While generating UX copy for the upcoming festival, it generated an average generic text, completely missing out on the nuance of the local holiday. This is a reminder to always localise or better yet itterate with additional input on important content.
• Sometimes, AI provided solutions may turn out to be too generic
  Anecdote: When I fed in a prompt for onboarding tips, AI responded with clichéd suggestions like “Provide skip button,” completly missing out on our app’s unique user journey. Human judgment and iterations helped better fit the flow to our context.

Something I learned about getting AI to work, beyond prompt engineering!

Working with AI is as much about process as prompts

Here are some practical implementation tips to follow the Smart Ideation Framework

• Use compact summaries
  Example: One project manager had the AI maintain a rolling summary during a long discovery session, making it easy to “catch up” latejoiners and resume after a day’s break.
• Generate reusable project briefs
  Anecdote: For a CRM revamp, the design lead asked AI to create a single-paragraph brief summarising all the pain points and goals. This brief became the team’s north star, shared across meetings.
• Ask for micro-recaps
  Example: Instead of scrolling up forever, our team simply asked: “AI, give us a 5-line recap so far”—helpful when session tokens run long or conversations get messy.
• Steer AI back on track
  Anecdote: Recently, a copywriter sensed the AI was making up features. Asking it to mark speculative points ensured only grounded suggestions went into the final draft.

Critical Evaluation: Questions to Keep AI Honest

Always interrogate AI suggestions with simple, direct questions:

• “What assumptions are you making?”
  Once, AI assumed our app users were all tech-savvy—correcting that flag shifted the design toward clarity and simplicity.
• “How would this solution fail?”
  When we asked this, AI flagged onboarding steps that required high bandwidth—crucial for our users in remote regions.

Key Takeaways

• AI = Creative Buddy
  Anecdote: As a mid-senior level designer, using AI to critique my ideas and solutions, has contributed to both building confidence in my skills as well as gaining more insight and perspective in various situations, establishing AI as a friendly “first-pass” reviewer for my work, before I present it. This helps build well rounded understanding, while making it easire to present my ideas to clients.
• Humans = Ethical & Cultural Compass
  Example: When AI suggested surpassing a checkbox for user, with regards to NCPR/NDNC for surpassing call directories,  I  pushed back, highlighting privacy and compliance issues.
• Approach = Smart Ideation Framework
  The best results are obtained when the empathy  of us humans and the structured creativity of AI work hand-in-hand rather than becoming completely dependent on whatever responses AI tools generate.Looking Ahead

Picture this: A team working on a next-gen wearable is stuck. The designer asks AI, “What can we learn from the hospitality sector about making guests feel at home?” In minutes, they’re applying hotel welcome rituals to onboarding, elevating the product’s warmth and distinctiveness. That’s the Smart Ideation Framework at its best, a constant, collaborative push for more human (and humane) experiences.

Beyond UX, AI in Performance Management

In one of my projects, 20/20 Insights, Gen AI  helped me analyse feedback trends to help me design for users, a better approach to setting goals . Some time in the previous month, an engineering manager discovered a communication gap in quarterly reviews.

When we think of AI now, we need to visualize it as more than just a tool, but a creative buddy, one who is  always there, ready to brainstorm (without having to look at it’s calendar), and most importantly is always ready to drive the design decisions and push us toward smarter, more empathetic and context appropriate UX solutions.